LiteLLM leads in npm + PyPI downloads at 4x; Google Gemini leads in active contributors with 5 (30d); Google Gemini leads in npm dependents with 675; Google Gemini leads in code adoption with 440 repos importing; both compete in Foundation Models. Updated daily with live metrics.
Data sources: npm, PyPI, GitHub, Crunchbase, Hacker News, Reddit, job boards. Methodology →
Google Gemini: 584.6K npm + PyPI downloads/mo, 98.8K GitHub stars, 5 active contributors, 675 dependents, 440 repos importing vs
LiteLLM: 2.2M npm + PyPI downloads/mo (+28% MoM), 40.0K GitHub stars, 5 active contributors, 1 dependents, 1 repos importing, $500K funded
Source: AI-Buzz Developer Adoption Index (DAI). Updated daily from 12 data sources. Methodology →
LiteLLM leads across 4 of 7 key metrics
Downloads (30d)
Disclosed Funding
GitHub Stars
Package Dependents
Repos Importing (code adoption)
HN Mentions (30d)
Momentum (0-100)
For production adoption: Google Gemini is depended on by 2.3K packages versus 2.3K for LiteLLM. LiteLLM leads in package downloads with 2.2M per month compared to 584.6K for Google Gemini (LiteLLM +28% MoM). Google Gemini appears in 440 GitHub repositories compared to 1 for LiteLLM.
For growth trajectory: Google Gemini has 98.8K GitHub stars versus 40.0K for LiteLLM. LiteLLM received 1 Hacker News mentions in the last 30 days.
For longevity/risk: LiteLLM has raised $500K in total disclosed funding. LiteLLM's most recent round was Seed. Google Gemini attracted 5 active contributors in the last 30 days compared to 5 for LiteLLM. both companies operate in the Foundation Models category.
LiteLLM leads on developer adoption with 2.2M monthly package downloads. Google Gemini counters with 98.8K GitHub stars. Both compete in the Foundation Models space. For a deeper look, visit each company's full profile for trend charts, funding rounds, and community sentiment data.
Both companies compete in Foundation Models. View all Foundation Models companies →
← Scroll to compare →
| Metric | Google Gemini Updated Mar 23 | Updated Mar 23 |
|---|---|---|
| Website | ai.google.dev → | litellm.ai → |
| Description | Google's multimodal AI model family and developer platform | Unified API for 100+ LLMs. Standard interface for multiple models. |
| Data Confidence Source: AI-BuzzUpdates: DailyNote: Composite score (0-100) based on field completeness, metric freshness, and identifier coverage. Higher = more reliable data.Methodology → | Good (77%) | Excellent (96%) |
| Signal Coverage Source: AI-BuzzUpdates: DailyNote: Number of non-null adoption metrics tracked. Companies with more signals have more reliable composite scores.Methodology → | 6/7 metrics | 7/7 metrics |
| Total Disclosed Funding Source: Public records / manual researchUpdates: WeeklyMethodology → | - | $500K |
| Last Funding | - | Seed Jan 2024 |
| Developer Adoption | ||
| Momentum | - | 34Moderate |
| npm Registry Downloads (30d) Source: npm registryUpdates: DailyNote: Includes all package installations including CI/CD pipelines and mirrorsMethodology → | 225.2K✓ | 271 |
| npm Trend (30d) | ||
| PyPI Registry Downloads (30d) Source: PyPI (Google BigQuery)Updates: DailyNote: Includes all package installations including CI/CD pipelines and mirrorsMethodology → | 359.5K | 2.2M✓ |
| PyPI Trend (30d) | ||
| Total Downloads - npm + PyPI (30d) Source: npm + PyPI registriesUpdates: DailyNote: Sum of npm and PyPI; excludes other package managersMethodology → | 584.6K | 2.2M+28%✓ |
| Active Contributors/Day Source: GitHub APIUpdates: DailyNote: Tracks designated public repos per company, not all company GitHub activityMethodology → | 5✓ | 5 |
| Contributors Trend | - | -17% |
| npm Dependents Source: Libraries.io APIUpdates: WeeklyNote: Counts direct and dev dependentsMethodology → | 675✓ | 1 |
| PyPI Dependents Source: Libraries.io APIUpdates: WeeklyNote: Counts direct reverse dependencies from PyPIMethodology → | 1.6K | 2.3K✓ |
| Code Adoption (repos) Source: ecosyste.ms Packages APIUpdates: DailyNote: Approximate count; excludes forksMethodology →Exclusive | 440✓ | 1 |
| HN Discussion Share Source: Hacker News (Algolia API)Updates: DailyNote: Share of HN mentions within the company's primary categoryMethodology → | - | 0.3% of HN mentions |
| Status | Public | Private |
| HN Mentions (30d) Source: Hacker News (Algolia API)Updates: DailyMethodology → | 0 | 1✓ |
| HN Mentions Trend (30d) | ||
| Reddit Mentions (30d) Source: Reddit (search API)Updates: DailyNote: Counts posts/comments mentioning the company by nameMethodology →Exclusive | 1✓ | 1 |
| Job Mentions (30d) Source: Job board aggregationUpdates: DailyNote: Counts job listings mentioning the company's technologyMethodology →Exclusive | - | 1 |
| GitHub Stars Source: GitHub APIUpdates: DailyNote: Stars are bookmarks — a popularity signal, not a usage indicatorMethodology → | 98.8K✓ | 40.0K |
| Founded | 2023 | 2023 |
| Primary Category | ||
| Data Last Updated | ||
Data sources: npm, PyPI, GitHub, Crunchbase, Hacker News, Reddit, job boards. Methodology →
Licensed CC BY 4.0. Free to cite with attribution.
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Compare metric trends across companies over time
Comparing npm Downloads over 30 days: Google Gemini (225.2K), LiteLLM (271).
Source: npm registry | Methodology | CC BY 4.0
| Date | Google Gemini | LiteLLM |
|---|---|---|
| Mar 1 | - | 881 |
| Mar 8 | - | 0 |
| Mar 15 | - | 0 |
| Mar 22 | 222.0K | 334 |
| Mar 23 | 225.2K | 271 |
